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Tag: importance

  • Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss

    Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss arXiv:2601.02440v1 Announce Type: new Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and…

  • One Permutation Is All You Need: Fast, Reliable Variable Importance and Model Stress-Testing

    One Permutation Is All You Need: Fast, Reliable Variable Importance and Model Stress-Testing arXiv:2512.13892v1 Announce Type: new Abstract: Reliable estimation of feature contributions in machine learning models is essential for trust, transparency and regulatory compliance, especially when models are proprietary or otherwise operate as black boxes. While permutation-based methods are a standard tool for this…

  • Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction

    Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction arXiv:2508.08724v1 Announce Type: new Abstract: Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challenge, which limits their utility in medical applications. Recently, model-agnostic methods have been proposed to measure…

  • Disentangled Feature Importance

    Disentangled Feature Importance arXiv:2507.00260v1 Announce Type: new Abstract: Feature importance quantification faces a fundamental challenge: when predictors are correlated, standard methods systematically underestimate their contributions. We prove that major existing approaches target identical population functionals under squared-error loss, revealing why they share this correlation-induced bias. To address this limitation, we introduce emph{Disentangled Feature Importance (DFI)},…

  • Hyperflows: Pruning Reveals the Importance of Weights

    Hyperflows: Pruning Reveals the Importance of Weights arXiv:2504.05349v1 Announce Type: new Abstract: Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most existing methods struggle to accurately assess the importance of individual weights due to their inherent interrelatedness, leading to poor performance, especially at extreme sparsity levels. We…

  • Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond

    Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond arXiv:2502.04575v1 Announce Type: new Abstract: Given an unnormalized probability density $piproptomathrm{e}^{-V}$, estimating its normalizing constant $Z=int_{mathbb{R}^d}mathrm{e}^{-V(x)}mathrm{d}x$ or free energy $F=-log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning. It is challenging especially in high dimensions…

  • Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect

    Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect arXiv:2501.16988v1 Announce Type: new Abstract: Interpreting black-box machine learning models is challenging due to their strong dependence on data and inherently non-parametric nature. This paper reintroduces the concept of importance through “Marginal Variable Importance Metric” (MVIM), a model-agnostic…